On the Importance of Data Quality Assessment of Crowdsourced Meteorological Data

This study reflects on the quality aspects of urban meteorological time series obtained by crowdsourcing, specifically air temperature and humidity data originating from personal weather stations (PWS) related implications for empirical numerical research. A number year-long hourly-based PWS were compared to authoritative selected areas in city Vienna, Austria. The results revealed a substantial amount erroneous occurrences, ranging singular sequential gaps prevalent faulty signals recorded data. These more prominent If not treated correctly, such datasets may be source errors that drive inaccurate inferences modelling could further critically misinform future mitigation measures aimed at alleviating pressures climate change urbanization.